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Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis.
Silva Santana, Laís; Borges Camargo Diniz, Jordana; Mothé Glioche Gasparri, Luisa; Buccaran Canto, Alessandra; Batista Dos Reis, Sávio; Santana Neville Ribeiro, Iuri; Gadelha Figueiredo, Eberval; Paulo Mota Telles, João.
Afiliação
  • Silva Santana L; School of Medicine, University of São Paulo, São Paulo, Brazil.
  • Borges Camargo Diniz J; Department of Neurology, Neurological Institute of Goiânia, Goiânia, Brazil.
  • Mothé Glioche Gasparri L; School of Medicine, Estácio de Sá University, Rio de Janeiro, Brazil.
  • Buccaran Canto A; School of Medicine, Max Planck University Center, Heidelberg, Germany.
  • Batista Dos Reis S; School of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Santana Neville Ribeiro I; Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
  • Gadelha Figueiredo E; Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
  • Paulo Mota Telles J; Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. Electronic address: joao.telles@fm.usp.br.
World Neurosurg ; 186: 204-218.e2, 2024 06.
Article em En | MEDLINE | ID: mdl-38580093
ABSTRACT

BACKGROUND:

Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types.

METHODS:

A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity.

RESULTS:

Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI] 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI 0.85-0.93) and 0.93 (95% CI 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI 0.97-1.00) and 0.94, (95% CI 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI 0.83-0.93) and 0.87 (95% CI 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications sensitivity of 0.99 (95% CI 0.99-1.00) and specificity of 0.99 (95% CI 0.98-1.00).

CONCLUSIONS:

ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Aprendizado de Máquina Limite: Humans Idioma: En Revista: World Neurosurg Assunto da revista: NEUROCIRURGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Aprendizado de Máquina Limite: Humans Idioma: En Revista: World Neurosurg Assunto da revista: NEUROCIRURGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos